Systems and Methods for Product Placement Optimization by Sensing Customer Traffic in Stores

1. An in-store customer traffic analysis system, comprising: a sensor network comprising a plurality of biometric sensors positioned within a retail store, the plurality of sensors comprising a first sensor; a plurality of displays comprising a first display; one or more memory devices storing instructions; and one or more hardware processors configured to execute the instructions to: receive, over an electronic communications network, a first sensor signal indicating that a first user has been recognized by the first sensor; extract a first user identifier from the first sensor signal; correlate the first sensor signal to the first display; generate a first foot traffic record associated with the first user identifier and the first display, based on the first sensor signal; store the first foot traffic record; determine a first user demographic group associated with the first user; obtain, based on an identifier of the first display, demographic-display data associated with the determined user demographic, the demographic-display data indicating an interest correlation between the first display and the user demographic in a time window; determine a recommendation to be displayed in the first display based on the demographic-display data; generate a processor-executable instruction to modify directional signage of the first display towards a product in the retail store according to the recommendation in an automated fashion; calculate a first score based on an amount of time the first user spends within a proximity of the modified first display and a direction from which the first user approached the first display; associate the first score with the first user identifier; store the associated score; and update, based on the amount of time the first user spends within a proximity of the modified first display, a second score indicating an interest of members of a second user demographic group associated with the first user in the first display in a time slot of a particular day.

2. The system of claim 1 , wherein the first sensor senses at least one of a face of the first user, a fingerprint of the first user, or a voice of the first user.

3. The system of claim 1 , wherein the one or more processors are further configured to execute instructions to; obtain a first user profile corresponding to the first user; identify a demographic category based on the first user profile; generate a display-demographic map indicating a correlation between the first display and the demographic category; and store the display-demographic map.

4. The system of claim 3 , wherein the one or more processors are further configured to execute instructions to: obtain an inventory listing associated with the retail store; and generate a recommendation for a product contained in the inventory listing, based on the display-demographic map.

5. The system of claim 4 , wherein the one or more processors are further configured to execute instructions to: provide the recommendation for the product contained in the inventory listing via a graphical user interface.

6. The system of claim 5 , wherein the one or more processors are further configured to execute instructions to: obtain a user input via the graphical user interface in response to providing the recommendation for the product contained in the inventory listing; generate a display instruction based on the obtained user input; and provide the generated display instruction via the graphical user interface.

7. The system of claim 1 , wherein the one or more processors are further configured to execute instructions to: receive a second sensor signal indicating that a second user has been recognized by the first sensor; extract a second user identifier from the second sensor signal; correlate the second sensor signal to the display; generate a second foot traffic record associated with the second user identifier and the display, based on the second sensor signal; and store the second generated foot traffic record, generate a recommendation for a product identified in the retail inventory listing based on a display-demographic map, wherein the display-demographic map is based on a first user profile related to the first user and a second user profile related to the second user.

8. A method for in-store customer traffic analysis, the method comprising; receiving, over an electronic communications network, a first sensor signal indicating that a first user has been recognized by a first biometric sensor of a sensor network in a retail store, the first sensor being associated with a first display in the retail store; extracting, by one or more processors, a first user identifier from the first sensor signal; correlating, by the one or more processors, the first sensor signal to the first display; generating, by the one or more processors, a first foot traffic record associated with the first user identifier and the first display, based on the first sensor signal; storing the first foot traffic record; determining a first user demographic group associated with the first user; obtaining, based on an identifier of the first display, demographic-display data associated with the determined user demographic, the demographic-display data indicating an interest correlation between the first display and the user demographic in a time window; determining a recommendation to be displayed in the first display based on the demographic-display data; generating a processor-executable instruction to directional signage of the first display towards a product in the retail store according to the recommendation in an automated fashion; calculating a first score based on an amount of time the first user spends within a proximity of the modified first display; associating the first score with the first user identifier and a direction from which the first user approached the display; storing the associated score; and updating, based on the amount of time the first user spends within a proximity of the modified first display, a second score indicating an interest of members of a second user demographic group associated with the first user in the first display in a time slot of a particular day.

9. The method of claim 8 , wherein the first sensor senses at least one of a face of the first user, a fingerprint of the first user, or a voice of the first user.

10. The method of claim 8 , further comprising: obtaining, by the one or more processors, a first user profile corresponding to the first user; identifying, by the one or more processors, a demographic category based on the first user profile; generating, by the one or more processors, a display-demographic map indicating a correlation between the first display and the demographic category; and storing the display-demographic map.

11. The method of claim 10 , further comprising; obtaining, by the one or more processors, an inventory listing associated with the retail store; and generating, by the one or more processors, a recommendation for a product contained in the inventory listing, based on the display-demographic map.

12. The method of claim 11 , wherein the one or more processors are further configured to execute instructions to: providing, by the one or more processors, the recommendation for the product contained in the inventory listing via a graphical user interface.

13. The method of claim 12 , wherein the one or more processors are further configured to execute instructions to: obtaining, by the one or more processors, a user input via the graphical user interface in response to providing the recommendation for the product contained in the inventory listing; generating, by the one or more processors, a display instruction based on the obtained user input; and providing, by the one or more processors, the generated display instruction via the graphical user interface.

14. The method of claim 8 , wherein the one or more processors are further configured to execute instructions to: receiving, by the one or more processors, a second sensor signal indicating that a second user has been recognized by the first sensor; extracting, by the one or more processors, a second user identifier from the second sensor signal; correlating, by the one or more processors, the second sensor signal to the first display; generating, by the one or more processors, a second foot traffic record associated with the second user identifier and the first display, based on the second sensor signal; storing the second generated foot traffic record; and generating, by the one or more processors, a recommendation for a product identified in the retail inventory listing based on a display-demographic map, wherein the display-demographic is based on a first user profile related to the first user and a second user profile related to the second user.

15. A non-transitory computer readable medium storing instructions that, when executed by one or more hardware processors, configure the one or more hardware processors to perform operations comprising: receiving, over an electronic communications network, a first sensor signal indicating that a first user has been recognized by a first biometric sensor of a sensor network in a retail store, the first sensor being associated with at least one display in the retail store; extracting a first user identifier from the first sensor signal; correlating the first sensor signal to the at least one display; generating a first foot traffic record associated with the first user identifier and the at least one display, based on the first sensor signal; storing the first foot traffic record; determining a user demographic of the first user; obtaining, based on an identifier of the first display, demographic-display data associated with the determined user demographic, the demographic-display data indicating an interest correlation between the at least one display and the user demographic in a time window; determining a recommendation to be displayed in the at least one display based on the demographic-display data; generating a processor-executable instruction to modify directional signage of the display towards a product in the retail store according to the recommendation in an automated fashion; calculating a first score based on an amount of time the first user spends within a proximity of the modified at least one display and a direction from which the first user approached the display associating the first score with the first user identifier; storing the associated score; and updating, based on the amount of time the first user spends within a proximity of the modified first display, a second score indicating an interest of members of a second user demographic group associated with the first user in the first display in a time slot of a particular day.

16. The non-transitory computer readable medium of claim 15 , the operations further comprising; receiving a second sensor signal indicating that a second user has been recognized by the first sensor in the retail store; extracting a second user identifier from the second sensor signal; correlating the second sensor signal to the at least one display; generating a second foot traffic record associated with the second user identifier and the at least one display, based on the second sensor signal; storing the second generated foot traffic record; and generating a recommendation for a product identified in the retail inventory listing based on a display-demographic map, wherein the display-demographic map is based on a first user profile related to the first user and a second user profile related to the second user.